In [1]:
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import plotly.express as px
%matplotlib inline
In [2]:
df=pd.read_csv("Unemployment_in_India.csv")
df.head()
Out[2]:
Region Date Frequency Estimated Unemployment Rate (%) Estimated Employed Estimated Labour Participation Rate (%) Area
0 Andhra Pradesh 31-05-2019 Monthly 3.65 11999139.0 43.24 Rural
1 Andhra Pradesh 30-06-2019 Monthly 3.05 11755881.0 42.05 Rural
2 Andhra Pradesh 31-07-2019 Monthly 3.75 12086707.0 43.50 Rural
3 Andhra Pradesh 31-08-2019 Monthly 3.32 12285693.0 43.97 Rural
4 Andhra Pradesh 30-09-2019 Monthly 5.17 12256762.0 44.68 Rural
In [3]:
df[' Date'] = pd.to_datetime(df[' Date'])
In [4]:
df.shape
Out[4]:
(768, 7)
In [5]:
df.describe()
Out[5]:
Estimated Unemployment Rate (%) Estimated Employed Estimated Labour Participation Rate (%)
count 740.000000 7.400000e+02 740.000000
mean 11.787946 7.204460e+06 42.630122
std 10.721298 8.087988e+06 8.111094
min 0.000000 4.942000e+04 13.330000
25% 4.657500 1.190404e+06 38.062500
50% 8.350000 4.744178e+06 41.160000
75% 15.887500 1.127549e+07 45.505000
max 76.740000 4.577751e+07 72.570000
In [6]:
df=df.dropna()
In [7]:
df.shape
Out[7]:
(740, 7)
In [8]:
Regions=df['Region'].unique()
Regions
Out[8]:
array(['Andhra Pradesh', 'Assam', 'Bihar', 'Chhattisgarh', 'Delhi', 'Goa',
       'Gujarat', 'Haryana', 'Himachal Pradesh', 'Jammu & Kashmir',
       'Jharkhand', 'Karnataka', 'Kerala', 'Madhya Pradesh',
       'Maharashtra', 'Meghalaya', 'Odisha', 'Puducherry', 'Punjab',
       'Rajasthan', 'Sikkim', 'Tamil Nadu', 'Telangana', 'Tripura',
       'Uttar Pradesh', 'Uttarakhand', 'West Bengal', 'Chandigarh'],
      dtype=object)
In [9]:
Area=df['Area'].unique()
Area
Out[9]:
array(['Rural', 'Urban'], dtype=object)
In [10]:
# convert column 'Date' to datetime format
df[' Date'] = pd.to_datetime(df[' Date'])

# extract range of dates
date_range = df[' Date'].max() - df[' Date'].min()

# display range
print(date_range)

396 days 00:00:00
In [11]:
print(df.isnull().sum())

Region                                      0
 Date                                       0
 Frequency                                  0
 Estimated Unemployment Rate (%)            0
 Estimated Employed                         0
 Estimated Labour Participation Rate (%)    0
Area                                        0
dtype: int64
In [12]:
plt.style.use("seaborn-whitegrid")
plt.figure(figsize=(12,10))
sns.heatmap(df.corr())
plt.show()
In [13]:
plt.title("Indian Employment Rate")
sns.histplot(x=" Estimated Employed",hue="Area",data=df)
plt.show()
In [14]:
plt.title("Indian Un-Employment Rate")
sns.histplot(x=" Estimated Unemployment Rate (%)",hue="Area",data=df)
plt.show()
In [15]:
plt.title("Indian Labour Participation Rate")
sns.histplot(x=" Estimated Labour Participation Rate (%)",hue="Area",data=df)
plt.show()
In [16]:
plt.title("Indian Employment Rate")
sns.histplot(x=" Estimated Employed",hue="Region",data=df)
plt.show()
In [17]:
plt.title("Indian Un-Employment Rate")
sns.histplot(x=" Estimated Unemployment Rate (%)",hue="Region",data=df)
plt.show()
In [18]:
plt.title("Indian Labour Participation Rate")
sns.histplot(x=" Estimated Labour Participation Rate (%)",hue="Region",data=df)
plt.show()
In [19]:
df.columns
Out[19]:
Index(['Region', ' Date', ' Frequency', ' Estimated Unemployment Rate (%)',
       ' Estimated Employed', ' Estimated Labour Participation Rate (%)',
       'Area'],
      dtype='object')
In [20]:
unemployment=df[["Region","Area"," Estimated Unemployment Rate (%)"]]
figure=px.sunburst(unemployment,path=["Area","Region"],values=" Estimated Unemployment Rate (%)",width=700,height=700,color_continuous_scale="RdYlGn",title="Unemployment Rate")
figure.show()

C:\Users\vinay\anaconda3\lib\site-packages\plotly\express\_core.py:1637: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  df_all_trees = df_all_trees.append(df_tree, ignore_index=True)
C:\Users\vinay\anaconda3\lib\site-packages\plotly\express\_core.py:1637: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  df_all_trees = df_all_trees.append(df_tree, ignore_index=True)
In [21]:
# Creating the bar chart
fig = px.bar(df, x=' Estimated Employed', y="Region", color='Area')

fig.show()
In [22]:
# Creating the bar chart
fig = px.bar(df, x=' Estimated Employed', y="Area", color='Region')

fig.show()
In [23]:
# plotting the scatter chart
fig = px.scatter(df, x=' Estimated Unemployment Rate (%)', y="Region", color='Area',symbol=' Frequency')

# showing the plot
fig.show()
In [24]:
# plotting the histogram
fig = px.histogram(df, x=" Estimated Labour Participation Rate (%)", color=' Frequency',nbins=50, histnorm='percent',barmode='overlay')

# showing the plot
fig.show()
In [25]:
fig = px.pie(df, values=" Estimated Labour Participation Rate (%)", names="Region",color_discrete_sequence=px.colors.sequential.RdBu,opacity=0.7, hole=0.5)
fig.show()
In [26]:
# plotting the boxplot
fig = px.box(df, x=" Estimated Labour Participation Rate (%)", y="Area", color=' Frequency',boxmode='group',notched=True)

# showing the plot
fig.show()
In [27]:
# plotting the figure
fig = px.scatter_3d(df, x=" Estimated Labour Participation Rate (%)", y="Region", z="Area", color=' Estimated Employed')
fig.show()
In [28]:
fig = px.line(df, x=' Date', y=' Estimated Unemployment Rate (%)', title='Time Series with Range Slider and Selectors')

fig.update_xaxes(
    rangeslider_visible=True,
    rangeselector=dict(
        buttons=list([
            dict(count=1, label="1m", step="month", stepmode="backward"),
            dict(count=6, label="6m", step="month", stepmode="backward"),
            dict(count=1, label="YTD", step="year", stepmode="todate"),
            dict(count=1, label="1y", step="year", stepmode="backward"),
            dict(step="all")
        ])
    )
)
fig.show()
In [ ]: